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Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain.
1903
Earlier work this paper cites.
1911
Earlier work this paper cites.
1912
Earlier work this paper cites.
J. Macqueen, “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Math. Statist. Probab. , 1967, pp. 281-297
1967
Earlier work this paper cites.
M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” in Proc. Adv. Neural Inf. Process. Syst. , Dec. 2001, pp. 585-591
2001
Earlier work this paper cites.
2002
Earlier work this paper cites.
L. K. Saul and S. T. Roweis, “Think globally, fit locally: unsupervised learning of low dimensional manifolds,” J. Mach. Learn. Res. , vol. 4, pp. 119–155, Dec. 2003
2003
Earlier work this paper cites.
2004
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. Rasch, B. Scholkopf, and A. J. Smola, “A kernel method for the two-sample-problem,” in Proc. Adv. in Neural Inf. Process. Syst. , 2007, pp. 513-520
2007
Earlier work this paper cites.
A. Goh and R. Vidal, “Segmenting motions of different types by unsupervised manifold clustering,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2007, pp. 1-6
2007
Earlier work this paper cites.
A. Goh and R. Vidal, “Clustering and dimensionality reduction on Riemannian manifolds,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2008, pp. 1-7
2008
Earlier work this paper cites.
L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res. , vol. 9, pp. 2579-2605, Nov. 2008
2008
Earlier work this paper cites.
M.-R. Amini, N. Usunier, and C. Goutte, “Learning from multiple partially observed views-an application to multilingual text categorization,” in Proc. Adv. in Neural Inf. Process. Syst. , Dec. 2009, pp. 28-36
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. , vol. 22, no. 10, pp. 1345-1359, Oct. 2010
2010
Earlier work this paper cites.
K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in Proc. Eur. Conf. Comput. Vis. , 2010, pp. 213-226
2010
Earlier work this paper cites.
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE Trans. Neural Netw. , vol. 22, no. 2, pp. 199-210, Feb. 2011
2011
Earlier work this paper cites.
L. Duan, I. W. Tsang, and D. Xu, “Domain transfer multiple kernel learning,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 3, pp. 465-479, Mar. 2012
2012
Earlier work this paper cites.
Z. Guo and Z. Wang, “Cross-domain object recognition via input-output kernel analysis,” IEEE Trans. Image Process. , vol. 22, no. 8, pp. 3108-3119, Aug. 2013
2013
Earlier work this paper cites.
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer feature learning with joint distribution adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Dec. 2013, pp. 2200-2207
2013
Earlier work this paper cites.
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, “Unsupervised visual domain adaptation using subspace alignment,” in Proc. Int. Conf. Comput. Vis. , Aug. 2013, pp. 2960-2967
2013
Earlier work this paper cites.
C.-X. Ren, D.-Q. Dai, K.-K. Huang, and Z.-R. Lai, “Transfer learning of structured representation for face recognition,” IEEE Trans. Image Process. , vol. 23, no. 12, pp. 5440-5454, Dec. 2014
2014
Earlier work this paper cites.
M. Long, J. Wang, G. Ding, S. J. Pan, and P. S. Yu, “Adaptation regularization: A general framework for transfer learning,” IEEE Trans. Knowl. Data Eng. , vol. 26, no. 5, pp. 1076-1089, May 2014
2014
Earlier work this paper cites.
F. Nie, X. Wang, and H. Huang, “Clustering and projected clustering with adaptive neighbors,” in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining , 2014, pp. 977-986
2014
Earlier work this paper cites.
J. Hoffman, E. Rodner, J. Donahue, B. Kulis, and K. Saenko, “Asymmetric and category invariant feature transformations for domain adaptation,” Int. J. Comput. Vis. , vol. 41, nos. 1-2, pp. 28-41, 2014
2014
Earlier work this paper cites.
W. Li, L. Duan, D. Xu, and I. W. Tsang, “Learning with augmented features for supervised and semi-supervised heterogeneous domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 36, no. 6, pp. 1134-1148, Jun. 2014
2014
Earlier work this paper cites.
M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer joint matching for unsupervised domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2014, pp. 1410-1417
2014
Earlier work this paper cites.
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “DeCAF: A deep convolutional activation feature for generic visual recognition,” in Proc. Int. Conf. Mach. Learn. , 2014, pp. 647-655
2014
Cited alongside, same era.
Q. Qiu and R. Chellappa, “Compositional dictionaries for domain adaptive face recognition,” IEEE Trans. Image Process. , vol. 24, no. 12, pp. 5152-5165, Dec. 2015
2015
Cited alongside, same era.
A. J. Ma, J. Li, P. C. Yuen, and P. Li, “Cross-domain person reidentification using domain adaptation ranking SVMs,” IEEE Trans. Image Process. , vol. 24, no. 5, pp. 1599-1613, May 2015
2015
Cited alongside, same era.
T. Yao, Y. Pan, Ch.-W. Ngo, H. Li, and T. Mei, “Semi-supervised domain adaptation with subspace learning for visual recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2015, pp. 2142-2150
2015
Cited alongside, same era.
J. Chang, G. Meng, L. Wang, S. Xiang and C. Pan, “Deep self-evolution clustering,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 4, pp. 809-823, Dec. 2018
2018
Later among the works it cites.
A. Rozantsev, M. Salzmann, and P. Fua, “Beyond sharing weights for deep domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 41, no. 4, pp. 801-814, Apr. 2019
2019
Later among the works it cites.
W. Wang, H. Wang, Z. X. Zhang, C. Zhang, and Y. Gao, “Semi-supervised domain adaptation via Fredholm integral based kernel methods,” Pattern Recognit. , vol. 85, pp. 185-197, Jan. 2019
2019
Later among the works it cites.
J. Liang, R. He, and T. Tan, “Aggregating randomized clustering-promoting invariant projections for domain adaptation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 41, no. 5, pp. 1027-1042, May 2019
2019
Later among the works it cites.
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M. Long, Y. Cao, J. Wang, and M. I. Jordan, “Learning transferable features with deep adaptation networks,” in Proc. Int. Conf. Mach. Learn. , Jul. 2015, pp. 97-105
2015
Cited alongside, same era.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in Proc. Int. Conf. Mach. Learn. , Jul. 2015, pp. 1180-1189
2015
Cited alongside, same era.
M. Xiao and Y. Guo, “Feature space independent semi-supervised domain adaptation via kernel matching,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 37, no. 1, pp. 54-66, Jan. 2015
2015
Cited alongside, same era.
T. Yao, Y. Pan, C.-W. Ngo, H. Li, and T. Mei, “Semi-supervised domain adaptation with subspace learning for visual recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2015, pp. 2142-2150
2015
Cited alongside, same era.
Y.-T. Hsieh, S.-Y. Tao, Y.-H. H. Tsai, Y.-R. Yeh, and Y.-C. F. Wang, “Recognizing heterogeneous cross-domain data via generalized joint distribution adaptation,” in Proc. IEEE Int. Conf. Multimedia Expo. , Jul. 2016, pp. 1-6
2016
Cited alongside, same era.
S. Chen, F. Zhou, and Q. Liao, “Visual domain adaptation using weighted subspace alignment,” in Proc. SPIE Int. Conf. Vis. Commun. Image Process. , Nov. 2016, pp. 1-4
2016
Cited alongside, same era.
M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Unsupervised domain adaptation with residual transfer networks,” in Proc. Adv. Neural Inf. Process. Syst. , 2016, pp. 136–144
2016
Cited alongside, same era.
Y.-H. H. Tsai, Y.-R. Yeh, and Y.-C. F. Wang, “Learning cross-domain landmarks for heterogeneous domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2016, pp. 5081-5090
2016
Cited alongside, same era.
J. Liang, R. He, Z. Sun and T. Tan, “Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2019, pp. 2975-2984
2019
Later among the works it cites.
A. Chadha, and Y. Andreopoulos, “Improved techniques for adversarial discriminative domain adaptation,” IEEE Trans. Image Process. , vol. 29, pp. 2622-2637, Nov. 2019
2019
Later among the works it cites.
C. Chen, W. Xie, W. Huang, Y. Rong, X. Ding, Y. Huang, T. Xu, and J. Huang, “Progressive feature alignment for unsupervised domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2019, pp. 627-636
2019
Later among the works it cites.
Y. Pan, T. Yao, Y. Li, Y. Wang, C.-W. Ngo, and T. Mei, “Transferrable prototypical networks for unsupervised domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2019, pp. 2239-2247
2019
Later among the works it cites.
J. Li, K. Lu, Z. Huang, L. Zhu, and H. Shen, “Heterogeneous domain adaptation through progressive alignment,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 30, no. 5, pp. 1381-1391, May 2019
2019
Later among the works it cites.
K. Zhan, F. Nie, J. Wang, and Y. Yang, “Multiview consensus graph clustering,” IEEE Trans. Image Process. , vol. 28, no. 3, pp. 1261-1270, Mar. 2019
2019
Later among the works it cites.
J. Li, M. Jing, K. Lu, L. Zhu and H. Shen, “Locality preserving joint transfer for domain adaptation,” IEEE Trans. Image Process. , vol. 28, no. 12, pp. 6103-6115, Dec. 2019
2019
Later among the works it cites.
J. Wang, Y. Chen, H. Yu, M. Huang, and Q. Yang, “Easy transfer learning by exploiting intra-domain structures,” in Proc. IEEE Int. Conf. Multimedia Expo. , Jul. 2019, pp. 1210-1215
2019
Later among the works it cites.
Q. Wang, P. Bu, and T. P. Breckon, “Unifying unsupervised domain adaptation and zero-shot visual recognition,” in Proc. Int. Joint Conf. Neural Netw. , Sep. 2019, pp. 14-19
2019
Later among the works it cites.
X. Wang, L. Li, W. Ye, M. Long, and J. Wang, “Transferable attention for domain adaptation,” in Proc. Amer. Assoc. Artif. Intell. Conf. , 2019, pp. 5345-5352
2019
Later among the works it cites.
V. K. Kurmi, S. Kumar, and V. P. Namboodiri, “Attending to discriminative certainty for domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2019, pp. 491-500
2019
Later among the works it cites.
S. Wu, J. Zhong, W. Cao, R. Li, Z. Yu, and H.-S. Wong, “Improving domain-specific classification by collaborative learning with adaptation networks,” in Proc. Amer. Assoc. Artif. Intell. Conf. , 2019, pp. 5450-5457
2019
Later among the works it cites.
J. Li, E. Chen, Z. Ding, L. Zhu, K. Lu, and Z. Huang, “Cycle-consistent conditional adversarial transfer networks,” in Proc. ACM Multimedia Conf. Multimedia Conf. , Oct. 2019, pp. 747-755
2019
Later among the works it cites.
S. Lee, D. Kim, N. Kim, and S.-G. Jeong, “Drop to adapt: learning discriminative features for unsupervised domain adaptation,” in Proc. Int. Conf. Comput. Vis. , Oct. 2019, pp. 91-100
2019
Later among the works it cites.
S. Li, C. Liu, Q. Lin, B. Xie, Z. Ding, G. Huang, and J. Tang, “Domain conditioned adaptation network,” in Proc. Amer. Assoc. Artif. Intell. Conf. , 2020, pp. 11386-11393
2020
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S. Cui, S. Wang, J. Zhuo, C. Su, Q. Huang, and Q. Tian, “Gradually vanishing bridge for adversarial domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2020, pp. 12455-12464
2020
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X. Gu, J. Sun, and Z. Xu, “Spherical space domain adaptation with robust pseudo-label loss,” in Proc. IEEE Conf.Comput. Vis. Pattern Recognit. , Jun. 2020, pp. 9101-9110
2020
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Y. Zhu, F. Zhuang, J. Wang, G. Ke, J.Chen, J. Bian, H. Xiong and Q. He, “Deep subdomain adaptation network for image classification,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1-10, May 2020
2020
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M. Chen, S. Zhao, H. Liu, and D. Cai, “Adversarial-learned loss for domain adaptation,” in Proc. Amer. Assoc. Artif. Intell. Conf. , 2020, pp. 3521-3528
2020
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B. Sun, J. Feng, and K. Saenko, “Return of frustratingly easy domain adaptation,” in Proc. Amer. Assoc. Artif. Intell. Conf. , 2016, pp. 2058-2065
2065
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B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2012, pp. 2066-2073
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